Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons

Ostojic, Srdjan

doi:10.1038/nn.3658

Cited by 285 publications

(463 citation statements)

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“…Asynchronous activity in an unstructured, sparsely connected network with weak synaptic couplings falls in a state such that an external input may favor information transmission in the target structure [20,21] and trigger the transition of an activity pattern in a neural network [22][23][24]. The random firing properties of PV1 neurons suggest that glutamate is released asynchronously at synaptic terminals and that "network driven" PV1 excitatory activity might contribute to achieve temporal frequency modulation of selected patterns of activity [25][26][27].…”

Section: Discussionmentioning

confidence: 99%

Discharge properties of neurons recorded in the parvalbumin-positive (PV1) nucleus of the rat lateral hypothalamus

Lintas¹

2014

Neuroscience Letters

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h i g h l i g h t s• Random-like firing characterized the majority of PV1 cells.• Oscillations in the delta range were observed in the posterior part of PV1 nucleus.• The asynchronous activity produces a network-driven effect on the PV1-target area.This study reports for the first time the extracellular activity recorded, in anesthetized rats, from cells located in an identified cluster of parvalbumin (PV)-positive neurons of the lateral hypothalamus forming the PV1-nucleus. Random-like firing characterized the majority (21/30) of the cells, termed regular cells, with a median firing rate of 1.7 spikes/s, Fano factor equal to 1, and evenly distributed along the rostrocaudal axis. Four cells exhibiting an oscillatory activity in the range 1.6-2.1 Hz were observed only in the posterior part of the PV1-nucleus. The asynchronous activity of PV1 neurons is likely to produce a "network-driven" effect on their main target within the periaqueductal gray matter. The hypothesis is raised that background random-like firing of PV1-nucleus is associated with functional network activity likely to contribute dynamic information related to condition transitions of awareness and non-conscious perception.

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Section: Discussionmentioning

confidence: 99%

Discharge properties of neurons recorded in the parvalbumin-positive (PV1) nucleus of the rat lateral hypothalamus

Lintas¹

2014

Neuroscience Letters

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“…Using this connectivity as their basis, a long series of works shows that by careful tampering with such a structure one may achieve a number of useful ends, with a notable flurry of recent activity [23][24][25][26][27][28][29][30][31].…”

Section: Contextmentioning

confidence: 99%

Renormalization of Collective Modes in Large-Scale Neural Dynamics

2017

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The bulk of studies of coupled oscillators use, as is appropriate in Physics, a global coupling constant controlling all individual interactions. However, because as the coupling is increased, the number of relevant degrees of freedom also increases, this setting conflates the strength of the coupling with the effective dimensionality of the resulting dynamics. We propose a coupling more appropriate to neural circuitry, where synaptic strengths are under biological, activity-dependent control and where the coupling strength and the dimensionality can be controlled separately. Here we study a set of N → ∞ strongly-and nonsymmetricallycoupled, dissipative, powered, rotational dynamical systems, and derive the equations of motion of the reduced system for dimensions 2 and 4. Our setting highlights the statistical structure of the eigenvectors of the connectivity matrix as the fundamental determinant of collective behavior, inheriting from this structure symmetries and singularities absent from the original microscopic dynamics.

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“…SPDV is an interesting feature in theoretical studies due to the easier experimental verification than other neuro-physiological features [17,18]. Neuron models in DDF, represented as in Eq.…”

Section: B Lif With Hypo-exponential Distributed Delay Functionmentioning

confidence: 99%

“…Fano-factor for a homogeneous Poisson process is exactly 1 [29], whereas, experimental data has Fano-factor distant from 1 [4,5,17]. In order to examine the effect of membrane potential delay on spiking activity and information processing of a neuron, we assume the rate code scheme of neuronal encoding and investigating the spike-count and the Fano-factor associated with spike sequence for considered neuron models.…”

Section: Spiking Activity Of a Neuron In Ddfmentioning

confidence: 99%

“…Spike-count is used to encode information whereas Fano-factor provides a measure of variability in spike sequence, which can be computed as fallow [22,29,30]. Fano-factor for this spike-sequence can be computed as Fano-factor:Fano-factor for a homogeneous Poisson process is exactly 1 [29], whereas, experimental data has Fano-factor distant from 1 [4,5,17]. In order to examine the effect of membrane potential delay on spiking activity and information processing of a neuron, we assume the rate code scheme of neuronal encoding and investigating the spike-count and the Fano-factor associated with spike sequence for considered neuron models.…”

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confidence: 99%

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Spiking Activity of a LIF Neuron in Distributed Delay Framework

Choudhary¹,

Singh²,

Solanki³

2016

IJIMAI

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-Evolution of membrane potential and spiking activity for a single leaky integrate-and-fire (LIF) neuron in distributed delay framework (DDF) is investigated. DDF provides a mechanism to incorporate memory element in terms of delay (kernel) function into a single neuron models. This investigation includes LIF neuron model with two different kinds of delay kernel functions, namely, gamma distributed delay kernel function and hypo-exponential distributed delay kernel function. Evolution of membrane potential for considered models is studied in terms of stationary state probability distribution (SPD). Stationary state probability distribution of membrane potential (SPDV) for considered neuron models are found asymptotically similar which is Gaussian distributed. In order to investigate the effect of membrane potential delay, rate code scheme for neuronal information processing is applied. Firing rate and Fano-factor for considered neuron models are calculated and standard LIF model is used for comparative study. It is noticed that distributed delay increases the spiking activity of a neuron. Increase in spiking activity of neuron in DDF is larger for hypo-exponential distributed delay function than gamma distributed delay function. Moreover, in case of hypo-exponential delay function, a LIF neuron generates spikes with Fano-factor less than 1.Keywords -Distributed Delay Framework, Fokker-Planck Equation, Gamma Distribution, Hypo-exponential Distribution, Stationary Probability Distribution, Spiking Activity. I. InTRoducTIonT heRe are a number of neuron models depending on biophysical and electrical properties of a neuron suggested in literature [1][2][3][4][5]. Among these neuron models, Leaky integrate-and-fire (LIF) model has become a backbone for theoretical as well as experimental investigation of neuronal dynamics due to its simplicity and analytical solvable capability [5,6,7]. This model is an RC-circuit equivalent representation of a neuron with additional spiking constraint and is widely used for mathematical explanation of bio-physical mechanism and information processing of neurons [1,2,4,8,9]. Based on some specific properties, many variants of LIF model are suggested in literature [1,5,7,8]. These neuron models explain neuronal dynamics adequately, but when we talk about memory, we have to rely on group of few neurons or neural networks. None of these single neuron models capture memory element. Recently, Karmeshu et. al. [10] have suggested a distributed delay framework for incorporating the effect of previous values of membrane potential (memory) on neuronal dynamics, in which, a kernel function is included in LIF model to capture the aggregate effect of previous values of membrane potential on its further evolution. It is a challenging problem to find an appropriate kernel function so that the resulting model can explain most of the variability in neuronal responses. To this end, Karmeshu et. al. [10] have investigated their proposed framework with exponential distributed delay kernel and...

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Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons

Cited by 285 publications

References 44 publications

Discharge properties of neurons recorded in the parvalbumin-positive (PV1) nucleus of the rat lateral hypothalamus

Discharge properties of neurons recorded in the parvalbumin-positive (PV1) nucleus of the rat lateral hypothalamus

Renormalization of Collective Modes in Large-Scale Neural Dynamics

Spiking Activity of a LIF Neuron in Distributed Delay Framework

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